{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
    "from keras.models import Model\n",
    "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing import sequence\n",
    "from keras.callbacks import EarlyStopping\n",
    "## 设置字体\n",
    "from matplotlib.font_manager import FontProperties\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>class_label</th>\n",
       "      <th>content</th>\n",
       "      <th>cutword</th>\n",
       "      <th>cutwordnum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>时政</td>\n",
       "      <td>韩美决定每年在黄海举行反潜联合军演(图)中新网10月19日电 据韩国《朝鲜日报》网站19日报...</td>\n",
       "      <td>韩美 黄海 反潜 联合 中新 中新网 新网 10 19 日电 韩国 朝鲜 日报 网站 19 ...</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>房产</td>\n",
       "      <td>王鹏：商业地产重要的三个环节董利：刚才蔡总介绍了一下台湾的便利店，其实便利店在改变着人们的生...</td>\n",
       "      <td>商业 商业地产 地产 三个 环节 介绍 下台 台湾 便利 便利店 便利 便利店 改变 生活 ...</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>房产</td>\n",
       "      <td>碧桂园股份掉期亏损净利降66%料今年销售额达190亿 杨国强对楼市表示“审慎乐观” 东方早报...</td>\n",
       "      <td>碧桂园 桂园 股份 亏损 净利 66 年销售额 销售 销售额 190 杨国强 国强 楼市 审...</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>房产</td>\n",
       "      <td>刘新虎：关于丽泽商务区的定位问题【刘新虎】：主要是为了学习，因为以前在丰台区开发过项目，20...</td>\n",
       "      <td>商务 商务区 定位 定位问题 学习 丰台 丰台区 台区 开发 发过 项目 2006 撤出 撤...</td>\n",
       "      <td>496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>财经</td>\n",
       "      <td>回顾2010年的邮市：各品种多版块都很给力老票精品很给力。2010年的春天，老票精品吹响了邮...</td>\n",
       "      <td>回顾 2010 邮市 品种 版块 精品 2010 春天 精品 吹响 邮市 反转 进军 军号 ...</td>\n",
       "      <td>1160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id class_label                                            content  \\\n",
       "0   0          时政  韩美决定每年在黄海举行反潜联合军演(图)中新网10月19日电 据韩国《朝鲜日报》网站19日报...   \n",
       "1   1          房产  王鹏：商业地产重要的三个环节董利：刚才蔡总介绍了一下台湾的便利店，其实便利店在改变着人们的生...   \n",
       "2   2          房产  碧桂园股份掉期亏损净利降66%料今年销售额达190亿 杨国强对楼市表示“审慎乐观” 东方早报...   \n",
       "3   3          房产  刘新虎：关于丽泽商务区的定位问题【刘新虎】：主要是为了学习，因为以前在丰台区开发过项目，20...   \n",
       "4   4          财经  回顾2010年的邮市：各品种多版块都很给力老票精品很给力。2010年的春天，老票精品吹响了邮...   \n",
       "\n",
       "                                             cutword cutwordnum  \n",
       "0  韩美 黄海 反潜 联合 中新 中新网 新网 10 19 日电 韩国 朝鲜 日报 网站 19 ...        223  \n",
       "1  商业 商业地产 地产 三个 环节 介绍 下台 台湾 便利 便利店 便利 便利店 改变 生活 ...        429  \n",
       "2  碧桂园 桂园 股份 亏损 净利 66 年销售额 销售 销售额 190 杨国强 国强 楼市 审...        305  \n",
       "3  商务 商务区 定位 定位问题 学习 丰台 丰台区 台区 开发 发过 项目 2006 撤出 撤...        496  \n",
       "4  回顾 2010 邮市 品种 版块 精品 2010 春天 精品 吹响 邮市 反转 进军 军号 ...       1160  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 读取测数据集\n",
    "# train_df = pd.read_csv('./cnews/cnews.train.txt', sep='\\t', names=['label','content'])\n",
    "# val_df = pd.read_csv(\"./cnews/cnews.val.txt\", sep='\\t', names=['label','content'])\n",
    "# test_df = pd.read_csv(\"./cnews/cnews.test.txt\", sep='\\t', names=['label','content'])\n",
    "train_df = pd.read_csv(\"../cnews/train_data_com2.csv\", names=['', '', ])\n",
    "val_df = pd.read_csv(\"../cnews/cnews_val.csv\")\n",
    "test_df = pd.read_csv(\"../cnews/cnews_test.csv\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'label'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-36e029c93983>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m## 查看训练集都有哪些标签\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcountplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Label'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\python\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5134\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5135\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5136\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5138\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'label'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 查看训练集都有哪些标签\n",
    "plt.figure()\n",
    "sns.countplot(train_df.label)\n",
    "plt.xlabel('Label',size = 10)\n",
    "plt.xticks(size = 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 分析训练集中词组数量的分布\n",
    "print(train_df.describe())\n",
    "plt.figure()\n",
    "plt.hist(train_df.cutwordnum,bins=100)\n",
    "plt.xlabel(\"词组长度\",size = 12)\n",
    "plt.ylabel(\"频数\",size = 12)\n",
    "plt.title(\"训练数据集\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 对数据集的标签数据进行编码\n",
    "train_y = train_df.label\n",
    "val_y = val_df.label\n",
    "test_y = test_df.label\n",
    "le = LabelEncoder()\n",
    "train_y = le.fit_transform(train_y).reshape(-1,1)\n",
    "val_y = le.transform(val_y).reshape(-1,1)\n",
    "test_y = le.transform(test_y).reshape(-1,1)\n",
    "\n",
    "## 对数据集的标签数据进行one-hot编码\n",
    "ohe = OneHotEncoder()\n",
    "train_y = ohe.fit_transform(train_y).toarray()\n",
    "val_y = ohe.transform(val_y).toarray()\n",
    "test_y = ohe.transform(test_y).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.1 Jieba分词\n",
    "# import jieba\n",
    "# def chinese_word_cut(mytext):  \n",
    "#     return \" \".join(jieba.cut(mytext))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df_cutword =train_df['content'].apply(chinese_word_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('我们', 1)\n",
      "('一个', 2)\n",
      "('中国', 3)\n",
      "('可以', 4)\n",
      "('基金', 5)\n",
      "('没有', 6)\n",
      "('自己', 7)\n",
      "('他们', 8)\n",
      "('市场', 9)\n",
      "('这个', 10)\n",
      "===================\n",
      "('马晓旭', 2)\n",
      "('意外', 1641)\n",
      "('受伤', 1948)\n",
      "('国奥', 148)\n",
      "('警惕', 385)\n",
      "('无奈', 1161)\n",
      "('大雨', 77)\n",
      "('格外', 529)\n",
      "('青睐', 1092)\n",
      "('殷家', 1)\n"
     ]
    }
   ],
   "source": [
    "## 使用Tokenizer对词组进行编码\n",
    "## 当我们创建了一个Tokenizer对象后，使用该对象的fit_on_texts()函数，以空格去识别每个词,\n",
    "## 可以将输入的文本中的每个词编号，编号是根据词频的，词频越大，编号越小。\n",
    "max_words = 5000\n",
    "max_len = 600\n",
    "tok = Tokenizer(num_words=max_words)  ## 使用的最大词语数为5000\n",
    "tok.fit_on_texts(train_df.cutword)\n",
    "\n",
    "## 使用word_index属性可以看到每次词对应的编码\n",
    "## 使用word_counts属性可以看到每个词对应的频数\n",
    "for ii,iterm in enumerate(tok.word_index.items()):\n",
    "    if ii < 10:\n",
    "        print(iterm)\n",
    "    else:\n",
    "        break\n",
    "print(\"===================\")  \n",
    "for ii,iterm in enumerate(tok.word_counts.items()):\n",
    "    if ii < 10:\n",
    "        print(iterm)\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 600)\n",
      "(5000, 600)\n",
      "(10000, 600)\n"
     ]
    }
   ],
   "source": [
    "## 对每个词编码之后，每句新闻中的每个词就可以用对应的编码表示，即每条新闻可以转变成一个向量了：\n",
    "train_seq = tok.texts_to_sequences(train_df.cutword)\n",
    "val_seq = tok.texts_to_sequences(val_df.cutword)\n",
    "test_seq = tok.texts_to_sequences(test_df.cutword)\n",
    "## 将每个序列调整为相同的长度\n",
    "train_seq_mat = sequence.pad_sequences(train_seq,maxlen=max_len)\n",
    "val_seq_mat = sequence.pad_sequences(val_seq,maxlen=max_len)\n",
    "test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)\n",
    "\n",
    "print(train_seq_mat.shape)\n",
    "print(val_seq_mat.shape)\n",
    "print(test_seq_mat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs (InputLayer)          [(None, 600)]             0         \n",
      "_________________________________________________________________\n",
      "embedding (Embedding)        (None, 600, 128)          640128    \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 128)               131584    \n",
      "_________________________________________________________________\n",
      "FC1 (Dense)                  (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "FC2 (Dense)                  (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 789,514\n",
      "Trainable params: 789,514\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## 定义LSTM模型\n",
    "inputs = Input(name='inputs',shape=[max_len])\n",
    "## Embedding(词汇表大小,batch大小,每个新闻的词长)\n",
    "layer = Embedding(max_words+1,128,input_length=max_len)(inputs)\n",
    "layer = LSTM(128)(layer)\n",
    "layer = Dense(128,activation=\"relu\",name=\"FC1\")(layer)\n",
    "layer = Dropout(0.5)(layer)\n",
    "layer = Dense(10,activation=\"softmax\",name=\"FC2\")(layer)\n",
    "model = Model(inputs=inputs,outputs=layer)\n",
    "model.summary()\n",
    "model.compile(loss=\"categorical_crossentropy\",optimizer=RMSprop(),metrics=[\"accuracy\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "391/391 [==============================] - 689s 2s/step - loss: 0.6889 - accuracy: 0.7906 - val_loss: 0.4543 - val_accuracy: 0.8780\n",
      "Epoch 2/10\n",
      "391/391 [==============================] - 736s 2s/step - loss: 0.2357 - accuracy: 0.9420 - val_loss: 0.4080 - val_accuracy: 0.9108\n",
      "Epoch 3/10\n",
      "391/391 [==============================] - 746s 2s/step - loss: 0.1754 - accuracy: 0.9589 - val_loss: 0.4649 - val_accuracy: 0.8958\n"
     ]
    }
   ],
   "source": [
    "## 模型训练\n",
    "model_fit = model.fit(train_seq_mat,train_y,batch_size=128,epochs=10,\n",
    "                      validation_data=(val_seq_mat,val_y),\n",
    "                      callbacks=[EarlyStopping(monitor='val_loss',min_delta=0.0001)] ## 当val-loss不再提升时停止训练\n",
    "                     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 482,
       "width": 482
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 对测试集进行预测\n",
    "test_pre = model.predict(test_seq_mat)\n",
    "\n",
    "## 评价预测效果，计算混淆矩阵\n",
    "confm = metrics.confusion_matrix(np.argmax(test_pre,axis=1),np.argmax(test_y,axis=1))\n",
    "## 混淆矩阵可视化\n",
    "Labname = [\"体育\",\"娱乐\",\"家居\",\"房产\",\"教育\",\"时尚\",\"时政\",\"游戏\",\"科技\",\"财经\"]\n",
    "plt.figure(figsize=(8,8))\n",
    "sns.heatmap(confm.T, square=True, annot=True,\n",
    "            fmt='d', cbar=False,linewidths=.8,\n",
    "            cmap=\"YlGnBu\")\n",
    "plt.xlabel('True label',size = 14)\n",
    "plt.ylabel('Predicted label',size = 14)\n",
    "plt.xticks(np.arange(10)+0.5,Labname,size = 12)\n",
    "plt.yticks(np.arange(10)+0.3,Labname,size = 12)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1000\n",
      "           1       0.96      0.98      0.97       982\n",
      "           2       0.77      0.96      0.85       802\n",
      "           3       0.97      0.99      0.98       973\n",
      "           4       0.84      0.97      0.90       868\n",
      "           5       0.95      0.93      0.94      1027\n",
      "           6       0.95      0.89      0.92      1071\n",
      "           7       0.99      0.94      0.96      1056\n",
      "           8       0.98      0.89      0.93      1102\n",
      "           9       0.99      0.89      0.94      1119\n",
      "\n",
      "    accuracy                           0.94     10000\n",
      "   macro avg       0.94      0.94      0.94     10000\n",
      "weighted avg       0.94      0.94      0.94     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(np.argmax(test_pre,axis=1),np.argmax(test_y,axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('../cnews/test_data.csv')\n",
    "test_seq = tok.texts_to_sequences(test_df.cutword)\n",
    "test_seq_mat = sequence.pad_sequences(test_seq,maxlen=max_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0, ..., 3312,  141,  352],\n",
       "       [   0,    0,    0, ...,  351,  352,  286],\n",
       "       [   0,    0,    0, ..., 2074,   76, 3273],\n",
       "       ...,\n",
       "       [   0,    0,    0, ...,  508, 1321,  286],\n",
       "       [   0,    0,    0, ..., 1954,  394, 1236],\n",
       "       [   0,    0,    0, ...,  338,  135,  113]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_seq_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.1478335e-05, 1.1090617e-05, 3.8403153e-04, ..., 1.8754633e-06,\n",
       "        3.1075272e-04, 9.9626499e-01],\n",
       "       [4.9726303e-09, 9.2510163e-06, 1.7613904e-05, ..., 3.6860530e-09,\n",
       "        4.5203620e-07, 3.7228356e-03],\n",
       "       [1.9974812e-05, 2.4273768e-07, 1.5530533e-05, ..., 2.2325510e-08,\n",
       "        4.6225956e-05, 9.9952209e-01],\n",
       "       ...,\n",
       "       [2.2947057e-07, 6.2982879e-05, 7.6255666e-05, ..., 6.9673575e-08,\n",
       "        5.8089954e-06, 1.6211238e-02],\n",
       "       [9.9851284e-05, 9.8228562e-01, 7.2769733e-04, ..., 1.7155062e-03,\n",
       "        5.1632972e-04, 2.0746801e-03],\n",
       "       [1.2093296e-05, 1.0404707e-06, 3.7205704e-05, ..., 5.8572905e-08,\n",
       "        5.6494799e-05, 9.9904162e-01]], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict = model.predict(test_seq_mat)\n",
    "predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.argmax(predict[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        9\n",
       "1        3\n",
       "2        9\n",
       "3        0\n",
       "4        8\n",
       "        ..\n",
       "19995    2\n",
       "19996    5\n",
       "19997    3\n",
       "19998    1\n",
       "19999    9\n",
       "Length: 20000, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictionn = []\n",
    "for i in range(len(predict)):\n",
    "     predictionn.append(int(np.argmax(predict[i])))\n",
    "prediction = pd.Series(predictionn)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['label'] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[\"体育\",\"娱乐\",\"家居\",\"房产\",\"教育\",\"时尚\",\"时政\",\"游戏\",\"科技\",\"财经\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "财经、时政\t高风险              6,9\n",
    "房产、科技\t中风险              3,8\n",
    "教育、时尚、游戏\t低风险       4,5,7\n",
    "家居、体育、娱乐\t可公开       2,0,1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fenlei(x):\n",
    "    if x == 0:\n",
    "        return '体育', '可公开'\n",
    "    if x == 1:\n",
    "        return '娱乐', '可公开'\n",
    "    if x == 2:\n",
    "        return '家居', '可公开'\n",
    "    if x == 3:\n",
    "        return '房产', '中风险'\n",
    "    if x == 4:\n",
    "        return '教育', '低风险'\n",
    "    if x == 5:\n",
    "        return '时尚', '低风险'\n",
    "    if x == 6:\n",
    "        return '财经', '高风险'\n",
    "    if x == 7:\n",
    "        return '游戏', '低风险'\n",
    "    if x == 8:\n",
    "        return '科技', '中风险'\n",
    "    if x == 9:\n",
    "        return '时政', '高风险'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "fin = test_df['label'].apply(func=fenlei)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        高风险\n",
       "1        中风险\n",
       "2        高风险\n",
       "3        可公开\n",
       "4        中风险\n",
       "        ... \n",
       "19995    可公开\n",
       "19996    低风险\n",
       "19997    中风险\n",
       "19998    可公开\n",
       "19999    高风险\n",
       "Length: 20000, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_label_a = []\n",
    "rank_label_a = []\n",
    "for i in range(len(fin)):\n",
    "    class_label_a.append(fin[i][0])\n",
    "    rank_label_a.append(fin[i][1])\n",
    "class_label = pd.Series(class_label_a)\n",
    "rank_label = pd.Series(rank_label_a)\n",
    "rank_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['class_label'] = class_label\n",
    "test_df['rank_label'] = rank_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_fin = test_df.drop(['content', 'cutword', 'cutwordnum', 'label'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>class_label</th>\n",
       "      <th>rank_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>时政</td>\n",
       "      <td>高风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>房产</td>\n",
       "      <td>中风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>时政</td>\n",
       "      <td>高风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>体育</td>\n",
       "      <td>可公开</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>科技</td>\n",
       "      <td>中风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19995</th>\n",
       "      <td>19995</td>\n",
       "      <td>家居</td>\n",
       "      <td>可公开</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19996</th>\n",
       "      <td>19996</td>\n",
       "      <td>时尚</td>\n",
       "      <td>低风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19997</th>\n",
       "      <td>19997</td>\n",
       "      <td>房产</td>\n",
       "      <td>中风险</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19998</th>\n",
       "      <td>19998</td>\n",
       "      <td>娱乐</td>\n",
       "      <td>可公开</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19999</th>\n",
       "      <td>19999</td>\n",
       "      <td>时政</td>\n",
       "      <td>高风险</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id class_label rank_label\n",
       "0          0          时政        高风险\n",
       "1          1          房产        中风险\n",
       "2          2          时政        高风险\n",
       "3          3          体育        可公开\n",
       "4          4          科技        中风险\n",
       "...      ...         ...        ...\n",
       "19995  19995          家居        可公开\n",
       "19996  19996          时尚        低风险\n",
       "19997  19997          房产        中风险\n",
       "19998  19998          娱乐        可公开\n",
       "19999  19999          时政        高风险\n",
       "\n",
       "[20000 rows x 3 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_fin.to_csv(\"../cnews/test_fin2.csv\", index=False, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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